Course Outline

AI in Credit Risk: Foundations and Opportunities for Government

  • Comparison of Traditional and AI-Powered Credit Risk Models
  • Challenges in Credit Evaluation: Bias, Explainability, and Fairness
  • Real-World Case Studies in AI for Lending

Data for Credit Scoring Models for Government

  • Sources: Transactional, Behavioral, and Alternative Data
  • Data Cleaning and Feature Engineering for Lending Decisions
  • Addressing Class Imbalance and Data Scarcity in Risk Prediction

Machine Learning for Credit Scoring for Government

  • Logistic Regression, Decision Trees, and Random Forests
  • Gradient Boosting (LightGBM, XGBoost) for Enhanced Scoring Accuracy
  • Model Training, Validation, and Tuning Techniques

AI-Driven Lending Workflows for Government

  • Automating Borrower Segmentation and Loan Risk Assessment
  • AI-Enhanced Underwriting and Approval Processes
  • Dynamic Pricing and Interest Rate Optimization Using Machine Learning

Model Interpretability and Responsible AI for Government

  • Explaining Predictions with SHAP and LIME
  • Ensuring Fairness in Credit Models: Bias Detection and Mitigation
  • Compliance with Regulatory Frameworks (e.g., ECOA, GDPR)

Generative AI in Lending Scenarios for Government

  • Utilizing Large Language Models for Application Review and Document Analysis
  • Prompt Engineering for Borrower Communication and Insights
  • Synthetic Data Generation for Model Testing

Strategy and Governance for AI in Credit for Government

  • Building Internal AI Capabilities versus External Solutions
  • Best Practices for Model Lifecycle Management and Governance
  • Future Trends: Real-Time Credit Scoring, Open Banking Integration

Summary and Next Steps for Government

Requirements

  • A comprehensive understanding of credit risk fundamentals for government and private sector applications
  • Experience with data analysis or business intelligence tools, suitable for enhancing decision-making processes
  • Familiarity with Python programming language, or a commitment to learning basic syntax to support analytical tasks

Audience

  • Lending managers for government and financial institutions
  • Credit analysts in both public and private sectors
  • Fintech innovators focused on advancing technology solutions for government and industry
 14 Hours

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